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Nonlinear Feature Extraction using Class-augmented Kernel PCA  

Park, Myoung-Soo (KIST, Human-centered Interaction and Robotics Research Center)
Oh, Sang-Rok (KIST, Human-centered Interaction and Robotics Research Center)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.48, no.5, 2011 , pp. 7-12 More about this Journal
Abstract
In this papwer, we propose a new feature extraction method, named as Class-augmented Kernel Principal Component Analysis (CA-KPCA), which can extract nonlinear features for classification. Among the subspace method that was being widely used for feature extraction, Class-augmented Principal Component Analysis (CA-PCA) is a recently one that can extract features for a accurate classification without computational difficulties of other methods such as Linear Discriminant Analysis (LDA). However, the features extracted by CA-PCA is still restricted to be in a linear subspace of the original data space, which limites the use of this method for various problems requiring nonlinear features. To resolve this limitation, we apply a kernel trick to develop a new version of CA-PCA to extract nonlinear features, and evaluate its performance by experiments using data sets in the UCI Machine Learning Repository.
Keywords
Class-augmented Kernel Principal Component Analysis; Nonlinear Feature Extraction; Disciminant Features;
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